Stereo-view and multi-view video formats are heavily investigated topics given their vast application potential. Depth Image Based Rendering (DIBR) system has been developed to improve Multiview Video Coding (MVC). Depth image is introduced to synthesize virtual views on the decoder side in this system. Depth image is a piecewise image, which is filled with sharp contours and smooth interior. Contours in a depth image show more importance than interior in view synthesis process. In order to improve the quality of the synthesized views and reduce the bitrate of depth image, a contour based coding strategy is proposed. First, depth image is divided into layers by different depth value intervals. Then regions, which are defined as the basic coding unit in this work, are segmented from each layer. The region is further divided into the contour and the interior. Two different procedures are employed to code contours and interiors respectively. A vector-based strategy is applied to code the contour lines. Straight lines in contours cost few of bits since they are regarded as vectors. Pixels, which are out of straight lines, are coded one by one. Depth values in the interior of a region are modeled by a linear or nonlinear formula. Coefficients in the formula are retrieved by regression. This process is called interior painting. Unlike conventional block based coding method, the residue between original frame and reconstructed frame (by contour rebuilt and interior painting) is not sent to decoder. In this proposal, contour is coded in a lossless way whereas interior is coded in a lossy way. Experimental results show that the proposed Contour Based Depth map Coding (CBDC) achieves a better performance than JMVC (reference software of MVC) in the high quality scenarios.

The H.264/AVC standard achieves significantly high coding efficiency if multiple block size Motion Estimation is adopted. However, the complexity of Motion Estimation and DCT is dramatically increased as a result. In previous work we propose an early mode decision algorithm to control the complexity, based on all-zero-blocks detection in 1616 size. In this paper, we improve the algorithm. Firstly, we propose to detect all-zero blocks in 1616, 88 and 44 sizes to simplify the course of mode decision. Secondly, we define the thresholds which are used to terminate motion estimation and mode decision in advance for these sizes. Last, we present the whole proposed algorithm. Experiments show that about 77% encoding time and 85% motion estimation time can be saved on average, which is better than state-of-the-art approaches.

Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

In this paper, Message Passing Interface (MPI) techniques are used to implement high-order full 3-D Navier-Stokes equations in multi-domain applications. A two-domain interface with five-point overlapping used previously is expanded to a multi-domain computation. There are normally two approaches for this expansion. One is to break up the domain into two parts through domain decomposition (say, one overlapping), then using MPI directives to further break up each domain into n parts. Another is to break the domain up into 2n parts with (2n-1) overlappings. In our present effort, the latter approach is used and finite-size overlappings are employed to exchange data between adjacent multi-dimensional sub-domains. It is an alternative way to parallelize the high-order full 3-D Navier-Stokes equations into multi-domain applications without adding much complexity. Results with high-order boundary treatments show consistency among multi-domain calculations and single-domain results.

This paper presents a novel data compression method for testing integrated circuits within the framework of pattern run-length coding. The test set is firstly divided into 2n-length patterns where n is a natural number. Then the compatibility of each pattern, which can be an external type, or an internal type, is analyzed. At last, the codeword of each pattern is generated according to its analysis result. Experimental results for large ISCAS89 benchmarks show that the proposed method can obtain a higher compression ratio than existing ones.

In this paper, we present a flexible distributed computing system in which it is very easy to add required computing components at any time. The system is an Internet-based solution, and mainly developed by Java and XML. Moreover, by implementing a new configuration of computing information that is setting up Public Information and Private Information, the system can accommodate various computing requests, and facilitate a flexible design. Additionally, to show the practical merit, as an example of signal processing, we presented how to apply our proposed system to selection of a suitable artificial neural network.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

Group key establishment is an important mechanism to construct a common session key for group communications. Conventional group key establishment protocols use an on-line trusted key generation center (KGC) to transfer the group key for each participant in each session. However, this approach requires that a trusted server be set up, and it incurs communication overhead costs. In this article, we address some security problems and drawbacks associated with existing group key establishment protocols. Besides, we use the concept of secret sharing scheme to propose a secure key transfer protocol to exclude impersonators from accessing the group communication. Our protocol can resist potential attacks and also reduce the overhead of system implementation. In addition, comparisons of the security analysis and functionality of our proposed protocol with some recent protocols are included in this article.

General exercise approaches are not convenient for some people in undertaking appropriate exercise due to the limited variety of present programs at existing exercise machines. Moreover, continuous support by one sports doctor is only available for a limited number of users. In this paper, therefore, we propose an Internet-based technical framework, which is designed on multi-tiered client/server architecture, for integrating and easily upgrading exercise programs. By applying the technical framework, a cycle ergometer health promotion system was developed for providing personally fitted. We also presented some facilities to assist sports doctors in quickly designing and remotely improving individual exercise protocols against cycle ergometer exercise based on a history database. Then we evaluated the Internet-based cycle ergometer system during two months of feasibility experiments for six elderly persons in terms of usability. As a result, the Internet-based cycle ergometer system was effective for continuously supporting the personal fitting procedure.

This paper presents a novel data compression method for testing integrated circuits within the selective dictionary coding framework. Due to the inverse value of dictionary indices made use of for the compatibility analysis with the heuristic algorithm utilized to solve the maximum clique problem, the method can obtain a higher compression ratio than existing ones.

To improve TCP throughput even if the maximum receiving window size is small, a TCP performance enhancing proxy (PEP) using a UDP-like packet sending policy with error control has been proposed. The PEP operates on a router along a TCP connection. When the PEP receives a data packet from the source host, it transmits the packet to the destination host, copies the packet into the local buffer (PEP buffer) in case the packets need to be transmitted and sends a premature ACK acknowledging receipt of the packet to the source host. In the PEP, the number of prematurely acknowledged packets in the PEP buffer is limited to a fixed threshold (watermark) value to avoid network congestion. Although the watermark value should be adjusted to changes in the network conditions, watermark adjusting algorithms have not been investigated. In this paper, we propose a watermark adjusting algorithm the goal of which is to maximize the throughput of each connection as much as possible without excessively suppressing the throughputs of the other connections. In our proposed algorithm, a newly established connection uses the initial watermark value of zero to avoid drastic network congestion and increases the value as long as its throughput increases. In addition, when a new connection is established, every already-established connection halves its watermark value to allow the newly established connection to use some portion of the bandwidth and increases again as long as its throughput increases. We compare the proposed algorithm (CW method) with other methods: the FW method that uses a fixed large watermark value and the NP method that does not use the PEP. Numerical results with respect to throughput and fairness showed that the CW method is generally superior to the other two methods.

Multi-view video can provide users with three-dimensional (3-D) and virtual reality perception through multiple viewing angles. In recent years, depth image-based rendering (DIBR) has been generally used to synthesize virtual view images in free viewpoint television (FTV) and 3-D video. To conceal the zero-region more accurately and improve the quality of a virtual view synthesized frame, an integrated hole-filling algorithm for view synthesis is proposed in this paper. The proposed algorithm contains five parts: an algorithm for distinguishing different regions, foreground and background boundary detection, texture image isophotes detection, a textural and structural isophote prediction algorithm, and an in-painting algorithm with gradient priority order. Based on the texture isophote prediction with a geometrical principle and the in-painting algorithm with a gradient priority order, the boundary information of the foreground is considerably clearer and the texture information in the zero-region can be concealed much more accurately than in previous works. The vision quality mainly depends on the distortion of the structural information. Experimental results indicate that the proposed algorithm improves not only the objective quality of the virtual image, but also its subjective quality considerably; human vision is also clearly improved based on the subjective results. In particular, the algorithm ensures the boundary contours of the foreground objects and the textural and structural information.

BEAN is a newly proposed lightweight stream cipher adopting Fibonacci FCSRs. It is designed for very constrained environments and aims at providing a balance between security, efficiency and cost. A weakness in BEAN was first found by Å gren and Hell in 2011, resulting in a key recovery attack slightly better than brute force. In this paper, we present new correlations between state and keystream with large statistical advantage, leading to a much more efficient key recovery attack. The time and data complexities of this attack are 257.53 and 259.94, respectively. Moreover, two new output functions are provided as alternatives, which are more efficent than the function used in BEAN and are immune to all attacks proposed on the cipher. Also, suggestions for improving the FCSRs are given.

RSPICE, a fast timing simulator for large digital MOS circuits, is presented in this paper. A new table-based region-wise linear MOS transistor model and the analytical solution of the generic sub-circuit primitive are applied to calculate the transient response of digital MOS circuits. The body effect of pass transistors is included in the MOS model and the floating capacitor network can be handled by this sub-circuit primitive as well. In RSPICE, MOS transistors with a DC path are grouped into a DC-connected block (DCCB), and DCCBs with a feedback path are combined as a strongly connected component (SCC). RSPICE orders SCCs by Tarjan's algorithm and simulates ordered SCCs one by one. DCCBs are basic cells in RSPICE and any DCCB can be mapped into one or more sub-circuit primitives. In order to calculate the transient response of these primitives analytically, RSPICE approximates the input signals of the primitive by piecewise linear functions. To compromise the simulation accuracy and run time, partial waveform and partial time convergent (PWPTC) combined with dynamic windowing technique is applied to simulate SCCs. Other key issues of RSPICE, such as circuit partition, pass-transistor and floating-capacitor processing, simulation-flow control and waveform modification are also discussed in detail. Compared with HSPICE , the simulation result of RSPICE is very accurate with an error less than 3%, but the speed is 1-2 orders over HSPICE.

As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.

A frontal and realistic face image was synthesized from a single profile face image. It has a wide range of applications in face recognition. Although the frontal face method based on deep learning has made substantial progress in recent years, there is still no guarantee that the generated face has identity consistency and illumination consistency in a significant posture. This paper proposes a novel pixel-based feature regression generative adversarial network (PFR-GAN), which can learn to recover local high-frequency details and preserve identity and illumination frontal face images in an uncontrolled environment. We first propose a Reslu block to obtain richer feature representation and improve the convergence speed of training. We then introduce a feature conversion module to reduce the artifacts caused by face rotation discrepancy, enhance image generation quality, and preserve more high-frequency details of the profile image. We also construct a 30,000 face pose dataset to learn about various uncontrolled field environments. Our dataset includes ages of different races and wild backgrounds, allowing us to handle other datasets and obtain better results. Finally, we introduce a discriminator used for recovering the facial structure of the frontal face images. Quantitative and qualitative experimental results show our PFR-GAN can generate high-quality and high-fidelity frontal face images, and our results are better than the state-of-art results.

Laser Doppler Vibrometers (LDVs) enable the acquisition of remote speech signals by measuring small-scale vibrations around a target. They are now widely used in the fields of information acquisition and national security. However, in remote speech detection, the coherent measurement signal is subject to environmental noise, making detecting and reconstructing speech signals challenging. To improve the detection distance and speech quality, this paper proposes a highly accurate real-time speech measurement method that can reconstruct speech from noisy coherent signals. First, the I/Q demodulation and arctangent phase discrimination are used to extract the phase transformation caused by the acoustic vibration from coherent signals. Then, an innovative smoothness criterion and a novel phase difference-based dynamic bilateral compensation phase unwrapping algorithm are used to remove any ambiguity caused by the arctangent phase discrimination in the previous step. This important innovation results in the highly accurate detection of phase jumps. After this, a further innovation is used to enhance the reconstructed speech by applying an improved waveform-based linear prediction coding method, together with adaptive spectral subtraction. This removes any impulsive or background noise. The accuracy and performance of the proposed method were validated by conducting extensive simulations and comparisons with existing techniques. The results show that the proposed algorithm can significantly improve the measurement of speech and the quality of reconstructed speech signals. The viability of the method was further assessed by undertaking a physical experiment, where LDV equipment was used to measure speech at a distance of 310m in an outdoor environment. The intelligibility rate for the reconstructed speech exceeded 95%, confirming the effectiveness and superiority of the method for long-distance laser speech measurement.

Time synchronization is an essential service for wireless sensor networks (WSNs). However, fixed-period time synchronization can not serve multiple users efficiently in terms of energy consumption. This paper proposes a lightweight precision-adaptive protocol for cluster-based multi-user networks. It consists of a basic average time synchronization algorithm and an adaptive control loop. The basic average time synchronization algorithm achieves 1 µs instantaneous synchronization error performance. It also prolongs re-synchronization period by taking the average of two specified nodes' local time to be cluster global time. The adaptive control loop realizes diverse levels of synchronization precision based on the proportional relationship between sync error and re-synchronization period. Experimental results show that the proposed precision-adaptive protocol can respond to the sync error bound change within 2 steps. It is faster than the exponential convergence of the adaptive protocols based on multiplicative iterations.

In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At last, the haze-free image can be restored by inverting the atmospheric scattering model. Additionally, brightness adjustment is also performed for preventing the dehazing result too dark. Experimental results on hazy images reveal that the proposed method can effectively remove the haze and yield dehazing results with vivid color and high scene visibility.

This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.